Honeycomb vs Vector: What are the differences?
What is Honeycomb? Observability for a distributed world--designed for high cardinality data and collaborative problem solving 🐝💖. We built Honeycomb to answer the hard questions that come up when you're trying to operate your software–to debug microservices, serverless, distributed systems, polyglot persistence, containers, and a world of fast, parallel deploys.
What is Vector? On-host performance monitoring framework which exposes hand picked high resolution metrics to every engineer’s browser, by Netflix. Vector provides a simple way for users to visualize and analyze system and application-level metrics in near real-time. It leverages the battle tested open source system monitoring framework, Performance Co-Pilot (PCP), layering on top a flexible and user-friendly UI. The UI polls metrics at up to 1 second resolution, rendering the data in completely configurable dashboards that simplify cross-metric correlation and analysis.
Honeycomb and Vector can be categorized as "Performance Monitoring" tools.
Vector is an open source tool with 3.17K GitHub stars and 230 GitHub forks. Here's a link to Vector's open source repository on GitHub.
What is Honeycomb?
What is Vector?
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Why do developers choose Honeycomb?
Why do developers choose Vector?
What are the cons of using Honeycomb?
What are the cons of using Vector?
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Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.
We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.